Explore global development with R

In this exercise, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks within this script.

Get the necessary packages

First, start with installing and activating the relevant packages tidyverse, gganimate, and gapminder if you do not have them already. Pay attention to what warning messages you get when installing gganimate, as your computer might need other packages than gifski and av

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
## Warning: pakke 'gganimate' blev bygget under R version 4.4.3
## Warning: pakke 'gifski' blev bygget under R version 4.4.3
## Warning: pakke 'av' blev bygget under R version 4.4.3
## Warning: pakke 'gapminder' blev bygget under R version 4.4.3

Look at the data and tackle the tasks

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility
options(scipen = 999)
ggplot(data=subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(aes(colour=continent)) +
  scale_x_log10() +
  ggtitle("1975")

#chatgpt for eliminating the scientific notation function and for mistakes in my code 
gapminder %>% 
  filter(year==1952) %>% 
  slice_max(gdpPercap, n=1)
## # A tibble: 1 × 6
##   country continent  year lifeExp    pop gdpPercap
##   <fct>   <fct>     <int>   <dbl>  <int>     <dbl>
## 1 Kuwait  Asia       1952    55.6 160000   108382.

…

We see an interesting spread with an outlier to the right. Explore who it is so you can answer question 2 below!

Next, you can generate a similar plot for 2007 and compare the differences

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(aes(colour = continent)) +
  scale_x_log10() +
  ggtitle("2007")

#used chatgpt to find a mistakes in my code 
gapminder %>% 
  filter(year==2007) %>% 
  slice_max(gdpPercap, n=5)
## # A tibble: 5 × 6
##   country       continent  year lifeExp       pop gdpPercap
##   <fct>         <fct>     <int>   <dbl>     <int>     <dbl>
## 1 Norway        Europe     2007    80.2   4627926    49357.
## 2 Kuwait        Asia       2007    77.6   2505559    47307.
## 3 Singapore     Asia       2007    80.0   4553009    47143.
## 4 United States Americas   2007    78.2 301139947    42952.
## 5 Ireland       Europe     2007    78.9   4109086    40676.

…

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Questions for the static figures:

  1. Answer: why does it make sense to have a log10 scale (scale_x_log10()) on the x axis? (hint: try to comment it out and observe the result) The function is useful because the values doesn’t change but the function makes it easier to read. spreads the values out.

  2. Answer: In Figure 1: Who is the outlier (the richest country in 1952) far right on the x axis? Kuwait

  3. Fix Figures 1 and 2: Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”. You want to eliminate it.)

  4. Answer: What are the five richest countries in the world in 2007? Norway, Kuwait, Singapore, United States and Ireland

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.

Also, there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

options(scipen=999)
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()  # convert x to log scale
anim

…

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.

options(scipen = 999)
anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)

…

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smooths the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() + # convert x to log scale
  transition_time(year, )
anim2

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages

Tasks for the animations:

  1. Can you add a title to one or both of the animations above that will change in sync with the animation? (Hint: search labeling for transition_states() and transition_time() functions respectively)
options(scipen=999)
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point(alpha = 0.7) +
  scale_x_log10(labels = scales::comma) +
  labs(title = "Global Development in {frame_time}",
       x = "GDP per capita (log scale)",
       y = "Life Expectancy",
       color = "Continent") +
  theme_minimal() +
  transition_time(year) +  
  ease_aes('linear')  

animate(anim, renderer = gifski_renderer())

  1. Can you made the axes’ labels and units more readable? Consider expanding the abbreviated labels as well as the scientific notation in the legend and x axis to whole numbers. Also, differentiate the countries from different continents by color
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point(alpha = 0.7) +
  scale_x_log10(labels = scales::comma) +
  scale_size_continuous(labels = scales::comma) +
  labs(title = "Global Development in {frame_time}",
       x = "GDP per capita (log scale)",
       y = "Life Expectancy",
       color = "Continent") +
  theme_minimal() +
  transition_time(year) +  
  ease_aes('linear')+ 
  theme(
    plot.title = element_text(size=18, face="bold"),
    axis.title.x = element_text(size=16, face="bold"),
    axis.title.y = element_text(size=16, face="bold"),
    axis.text.x = element_text(size=14),
    axis.text.y = element_text(size=14))

animate(anim, renderer = gifski_renderer())

Final Question

  1. Is the world a better place today than it was in the year you were born? Answer this question using the gapminder data. Define better either as more prosperous, more free, more healthy, or suggest another measure that you can get from gapminder. Submit a 250 word answer with an illustration to Brightspace. Include a URL in your Brightspace submission that links to the coded solutions in Github. [Hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset or download more historical data at https://www.gapminder.org/data/ ]
birth_year <- 2002
gapminder %>% 
  filter(year %in% c(birth_year, 2007)) %>%
  group_by(year) %>% 
  summarise(avg_lifeExp=mean(lifeExp, na.rm=TRUE), avg_gdpPercap=mean(gdpPercap, na.rm=TRUE))
## # A tibble: 2 × 3
##    year avg_lifeExp avg_gdpPercap
##   <int>       <dbl>         <dbl>
## 1  2002        65.7         9918.
## 2  2007        67.0        11680.
ggplot(gapminder %>% filter(year %in% c(birth_year, 2007)),
       aes(x = factor(year), y = lifeExp, fill = continent)) +
  geom_boxplot() +
  labs(title = "Life Expectancy Over Time",
       x = "Year",
       y = "Life Expectancy",
       fill = "Continent") +
  theme_minimal()

The question was “Is the World a better place today than the year I was born?”, and this can be answered with various factors in mind. I chose to plot Life Expectancy, and from my illustration and coding I can conclude that the world now compared to the year 2002 is somewhat a better place seen from the perspective of life expectancy. It has not increased a lot. It is also only a 5 year difference, which does have an effect on the results of the development. The higher life expectancy can be caused by a lot of factors, such as better healthcare, living standards, economy etc. Just to look at a broader perspective, I would like to point out that the average gdp also increased from 2002 to 2007. This contributes to the world being a better place. Over time both of these numbers have increased over time, not just from 2002 to 2007 but also before that. As I wrote, 5 years is not a long time, and does not give full insight to Global development, than if you had chosen for example 1950. If we take into count how the life expectancy in Kuwait - from the first questions - in 1952, had a life expectancy at about 55, the increase looks a little more dramatic with a 12 years difference.
But all in all, the world is a better place for a longer life and more general wealth, but does that mean it is a better world in other ways.

In this assignment, I have used chatgpt for correcting mistakes in my coding, and for help solving and understanding the question